Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Moorman, Liza Prudente

A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below: • To promote trust, the science must be understandable. • To enhance uptake, the workflow should not be impacted greatly. • To maximize buy-in, engagement at all levels is important. • To ensure relevance, the education must be tailored to the clinical role and hospital culture. • To lead to clinical action, the information must integrate into clinical care. • To promote sustainability, there should be periodic support interactions after formal implementation.